In recent years deep reinforcement learning have demonstrated the ability to solve complex control tasks such as playing strategic games with super-human performance [1]}, [2]}, controlling complex robotic systems [3]}, and others. However, most of popular reinforcement learning algorithms require a vast amount of environment interaction experience to achieve this result. For example, OpenAI Five [2]} takes more than ten thousand years of simulated game time to master Dota 2. This limitation prevents reinforcement learning from being widely applied to real-world tasks.
